LiDAR reveals a preference for intermediate visibility by a forest-dwelling ungulate species.

Visibility (viewshed) plays a significant and diverse role in animals' behaviour and fitness. Understanding how visibility influences animal behaviour requires the measurement of habitat visibility at spatial scales commensurate to individual animal choices. However, measuring habitat visibility at a fine spatial scale over a landscape is a challenge, particularly in highly heterogeneous landscapes (e.g. forests). As a result, our ability to model the influence of fine-scale visibility on animal behaviour has been impeded or limited. In this study, we demonstrate the application of the concept of three-dimensional (3D) cumulative viewshed in the study of animal spatial behaviour at a landscape level. Specifically, we employed a newly described approach that combines terrestrial and airborne light detection and ranging (LiDAR) to measure fine-scale habitat visibility (3D cumulative viewshed) on a continuous scale in forested landscapes. We applied the LiDAR-derived visibility to investigate how visibility in forests affects the summer habitat selection and the movement of 20 GPS-collared female red deer Cervus elaphus in a temperate forest in Germany. We used integrated step selection analysis to determine whether red deer show any preference for fine-scale habitat visibility and whether visibility is related to the rate of movement of red deer. We found that red deer selected intermediate habitat visibility. Their preferred level of visibility during the day was substantially lower than that of night and twilight, whereas the preference was not significantly different between night and twilight. In addition, red deer moved faster in high-visibility areas, possibly mainly to avoid predation and anthropogenic risk. Furthermore, red deer moved most rapidly between locations in the twilight. For the first time, the preference for intermediate habitat visibility and the adaption of movement rate to fine-scale visibility by a forest-dwelling ungulate species at a landscape scale was revealed. The LiDAR technique used in this study offers fine-scale habitat visibility at the landscape level in forest ecosystems, which would be of broader interest in the fields of animal ecology and behaviour.

Habitat visibility affects the behavioral response of a large herbivore to human disturbance in forest landscapes.

Wildlife can perceive humans as predators and human disturbance, whether lethal (e.g., hunting) or non-lethal (e.g., hiking, biking, and skiing), triggers antipredator behavior among prey. Visibility is the property that relates habitat structure to accessibility of visual information that allows animals to detect predators and evaluate predation risk. Thus, the visibility of a habitat (hereafter referred to as habitat visibility) for prey species alters the perceived risk of predation and therefore has a strong influence on their antipredator behavior. Yet, knowledge of how habitat visibility affects the response of animals to different types of human disturbance is limited, partly, because it is challenging to measure habitat visibility for animals at a fine spatial scale over a landscape, particularly in highly heterogeneous landscapes (e.g., forests). In this study, we employed a newly described approach that combines terrestrial and airborne LiDAR to contiguously measure fine-scale habitat visibility in a forested landscape. We applied the LiDAR-derived habitat visibility to examine how habitat visibility in forests affects the summer space use of 20 GPS-collared female red deer (Cervus elaphus) modeled with integrated step-selection functions in the Bavarian Forest National Park, Germany when exposed to various types of human disturbance including recreational activities, forest roads, hiking trails, and hunting. We found that red deer in our study area avoided areas with higher all types of human disturbance, especially during daylight hours. Furthermore, habitat visibility significantly modified the use of space by red deer in response to human recreational activities, forest roads, and hiking trails, but not to the hunting area. Red deer tended to tolerate a higher intensity of human recreational activities and to use areas closer to forest roads or hiking trails when they have lower habitat visibility (i.e., more cover). Our findings highlight the importance of considering visual perception when studying the response of wild animals to human disturbance. We emphasize the potential to mitigate negative consequences of human disturbance on wildlife, through measures such as maintaining vegetative buffers around recreational infrastructure (e.g., roads and skiing tracks) in order to reduce habitat visibility around areas frequented by humans.

Smallholder farms as stepping stone corridors for crop-raiding elephant in northern Tanzania: integration of Bayesian expert system and network simulator.

Crop-raiding elephants affect local livelihoods, undermining conservation efforts. Yet, crop-raiding patterns are poorly understood, making prediction and protection difficult. We hypothesized that raiding elephants use corridors between daytime refuges and farmland. Elephant counts, crop-raiding records, household surveys, Bayesian expert system, and least-cost path simulation were used to predict four alternative categories of daily corridors: (1) footpaths, (2) dry river beds, (3) stepping stones along scattered small farms, and (4) trajectories of shortest distance to refuges. The corridor alignments were compared in terms of their minimum cumulative resistance to elephant movement and related to crop-raiding zones quantified by a kernel density function. The "stepping stone" corridors predicted the crop-raiding patterns. Elephant presence was confirmed along these corridors, demonstrating that small farms located between refuges and contiguous farmland increase habitat connectivity for elephant. Our analysis successfully predicted elephant occurrence in farmland where daytime counts failed to detect nocturnal presence. These results have conservation management implications.

Eutrophication of mangroves linked to depletion of foliar and soil base cations.

There is growing concern that increasing eutrophication causes degradation of coastal ecosystems. Studies in terrestrial ecosystems have shown that increasing the concentration of nitrogen in soils contributes to the acidification process, which leads to leaching of base cations. To test the effects of eutrophication on the availability of base cations in mangroves, we compared paired leaf and soil nutrient levels sampled in Nypa fruticans and Rhizophora spp. on a severely disturbed, i.e. nutrient loaded, site (Mahakam delta) with samples from an undisturbed, near-pristine site (Berau delta) in East Kalimantan, Indonesia. The findings indicate that under pristine conditions, the availability of base cations in mangrove soils is determined largely by salinity. Anthropogenic disturbances on the Mahakam site have resulted in eutrophication, which is related to lower levels of foliar and soil base cations. Path analysis suggests that increasing soil nitrogen reduces soil pH, which in turn reduces the levels of foliar and soil base cations in mangroves.

High-throughput Soxhlet extraction method applied for analysis of leaf lignocellulose and non-structural substances.

The traditional Soxhlet extraction method is commonly employed to extract soluble components from non-soluble components in a solid matrix, for example, non-structural substances in biomass samples that can be separated from structural lignocellulosic compounds in biomass samples. Conventional laboratory procedures for such extractions typically involve a low sample throughput, with each run being performed individually, resulting in time-consuming and labour-intensive processes, making them impractical for analysing large sample sets. In research fields such as Earth Observation in Forest Ecosystems, extensive fieldwork sampling is required across large study areas, resulting in a substantial number of leaf samples, each with limited mass. In this study, an innovative adaptation of the conventional National Renewable Energy Laboratory (NREL) Soxhlet method is developed to create a high-throughput mini-Soxhlet apparatus that enables the simultaneous extraction of up to nineteen samples, each with a mass of 0.3 g per sample. With this adaptation, we measured the lignocellulose and extractive in 343 leaf samples collected from four temperate forest tree species. This modified approach enhances versatility and can be applied to all solid-liquid extractions and various types of vegetation tissues, such as tree leaves, shrubs, crops, feedstock, and other non-woody samples.•The solid-liquid extraction method has been implemented in a heating block facilitating 19 small flasks to measure multiple samples simultaneously while requiring only a small sample mass.•The apparatus set-up was constructed using an alumina heating block mounted on a standard laboratory heating plate. Boiling flask tubes were placed in the heating block and equipped with condenser caps and filters on glass rods on which the solid samples were placed.•The adjustments made the method suitable for application to diverse vegetation tissues and non-woody sample types. It holds particular appeal for research areas that necessitate a high sample number.

Forest top canopy bacterial communities are influenced by elevation and host tree traits.

BACKGROUND: The phyllosphere microbiome is crucial for plant health and ecosystem functioning. While host species play a determining role in shaping the phyllosphere microbiome, host trees of the same species that are subjected to different environmental conditions can still exhibit large degrees of variation in their microbiome diversity and composition. Whether these intra-specific variations in phyllosphere microbiome diversity and composition can be observed over the broader expanse of forest landscapes remains unclear. In this study, we aim to assess the variation in the top canopy phyllosphere bacterial communities between and within host tree species in the temperate European forests, focusing on Fagus sylvatica (European beech) and Picea abies (Norway spruce). RESULTS: We profiled the bacterial diversity, composition, driving factors, and discriminant taxa in the top canopy phyllosphere of 211 trees in two temperate forests, Veluwe National Parks, the Netherlands and Bavarian Forest National Park, Germany. We found the bacterial communities were primarily shaped by host species, and large variation existed within beech and spruce. While we showed that there was a core microbiome in all tree species examined, community composition varied with elevation, tree diameter at breast height, and leaf-specific traits (e.g., chlorophyll and P content). These driving factors of bacterial community composition also correlated with the relative abundance of specific bacterial families. CONCLUSIONS: While our results underscored the importance of host species, we demonstrated a substantial range of variation in phyllosphere bacterial diversity and composition within a host species. Drivers of these variations have implications at both the individual host tree level, where the bacterial communities differed based on tree traits, and at the broader forest landscape level, where drivers like certain highly plastic leaf traits can potentially link forest canopy bacterial community variations to forest ecosystem processes. We eventually showed close associations between forest canopy phyllosphere bacterial communities and host trees exist, and the consistent patterns emerging from these associations are critical for host plant functioning.

Tracing glacial refugia of Triturus newts based on mitochondrial DNA phylogeography and species distribution modeling.

INTRODUCTION: The major climatic oscillations during the Quaternary Ice Age heavily influenced the distribution of species and left their mark on intraspecific genetic diversity. Past range shifts can be reconstructed with the aid of species distribution modeling and phylogeographical analyses. We test the responses of the different members of the genus Triturus (i.e. the marbled and crested newts) as the climate shifted from the previous glacial period (the Last Glacial Maximum, ~21 Ka) to the current interglacial. RESULTS: We present the results of a dense mitochondrial DNA phylogeography (visualizing genetic diversity within and divergence among populations) and species distribution modeling (using two different climate simulations) for the nine Triturus species on composite maps. CONCLUSIONS: The combined use of species distribution modeling and mitochondrial phylogeography provides insight in the glacial contraction and postglacial expansion of Triturus. The combined use of the two independent techniques yields a more complete understanding of the historical biogeography of Triturus than both approaches would on their own. Triturus newts generally conform to the 'southern richness and northern purity' paradigm, but we also find more intricate patterns, such as the absence of genetic variation and suitable area at the Last Glacial Maximum (T. dobrogicus), an 'extra-Mediterranean' refugium in the Carpathian Basin (T. cristatus), and areas where species displaced one another postglacially (e.g. T. macedonicus and western T. karelinii). We provide a biogeographical scenario for Triturus, showing the positions of glacial refugia, the regions that were postglacially colonized and the areas where species displaced one another as they shifted their ranges.

Sensing solutions for collecting spatio-temporal data for wildlife monitoring applications: a review.

Movement ecology is a field which places movement as a basis for understanding animal behavior. To realize this concept, ecologists rely on data collection technologies providing spatio-temporal data in order to analyze movement. Recently, wireless sensor networks have offered new opportunities for data collection from remote places through multi-hop communication and collaborative capability of the nodes. Several technologies can be used in such networks for sensing purposes and for collecting spatio-temporal data from animals. In this paper, we investigate and review technological solutions which can be used for collecting data for wildlife monitoring. Our aim is to provide an overview of different sensing technologies used for wildlife monitoring and to review their capabilities in terms of data they provide for modeling movement behavior of animals.

Optimization of wildlife management in a large game reserve through waterpoints manipulation: a bio-economic analysis.

Surface water is one of the constraining resources for herbivore populations in semi-arid regions. Artificial waterpoints are constructed by wildlife managers to supplement natural water supplies, to support herbivore populations. The aim of this paper is to analyse how a landowner may realize his ecological and economic goals by manipulating waterpoints for the management of an elephant population, a water-dependent species in the presence of water-independent species. We develop a theoretical bio-economic framework to analyse the optimization of wildlife management objectives (in this case revenue generation from both consumptive and non-consumptive use and biodiversity conservation), using waterpoint construction as a control variable. The model provides a bio-economic framework for analysing optimization problems where a control has direct effects on one herbivore species but indirect effects on the other. A landowner may be interested only in maximization of profits either from elephant offtake and/or tourism revenue, ignoring the negative effects that could be brought about by elephants to biodiversity. If the landowner does not take the indirect effects of waterpoints into consideration, then the game reserve management, as the authority entrusted with the sustainable management of the game reserve, might use economic instruments such as subsidies or taxes to the landowners to enforce sound waterpoint management.

Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape.

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.

Soil biotic impact on plant species shoot chemistry and hyperspectral reflectance patterns.

Recent studies revealed that plant-soil biotic interactions may cause changes in above-ground plant chemistry. It would be a new step in below-ground-above-ground interaction research if such above-ground chemistry changes could be efficiently detected. Here we test how hyperspectral reflectance may be used to study such plant-soil biotic interactions in a nondestructive and rapid way. The native plant species Jacobaea vulgaris and Jacobaea erucifolius, and the exotic invader Senecio inaequidens were grown in different soil biotic conditions. Biomass, chemical content and shoot reflectance between 400 and 2500 nm wavelengths were determined. The data were analysed with multivariate statistics. Exposing the plants to soil biota enhanced the content of defence compounds. The highest increase (400%) was observed for the exotic invader S. inaequidens. Chemical and spectral data enabled plant species to be classified with an accuracy > 85%. Plants grown in different soil conditions were classified with 50-60% correctness. Our data suggest that soil microorganisms can affect plant chemistry and spectral reflectance. Further studies should test the potential to study plant-soil biotic interactions in the field. Such techniques could help to monitor, among other things, where invasive exotic plant species develop biotic resistance or the development of hotspots of crop soil diseases.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 µm) to discriminate vegetation species.

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.

Linking the past and present to predict the distribution of Asian crested ibis (Nipponia nippon) under global changes.

Understanding how species' ecological niches adapt to environmental changes through time is critical for predicting the effect of future global change on endangered species. Yet few studies have incorporated knowledge of past niche shifting into the assessment of species' future fate in a changing world. In this study, we integrated the ecological niche dynamics into the species distribution modeling of the Asian crested ibis (Nipponia nippon) in East Asia. Specifically, we compared historical and present ecological niches of crested ibis in four-dimensional environmental space based on species occurrence and environmental data. We then employed a multi-temporal ecological niche model to estimate the potential geographical distribution of crested ibis under future climate and land-use changes. Our results show that crested ibis retained similar though not identical ecological niches over time. Compared to the historical baseline range, the current suitable habitat for crested ibis has been reduced by 39.6%. The effects of human activity outweigh those of climate change regarding the distribution of crested ibis. We conclude that the ecological niche of crested ibis was tended to be conservative, and future potentially suitable habitat may encounter northeastward and northwestward shift, and possibly expand by 18.7% referred to the historical range. The findings of our study are of clear importance for the conservation and successful reintroduction of crested ibis in East Asia.

The Spectral Species Concept in Living Color.

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

Soil nutrient status determines how elephant utilize trees and shape environments.

1. Elucidation of the mechanism determining the spatial scale of patch selection by herbivores has been complicated by the way in which resource availability at a specific scale is measured and by vigilance behaviour of the herbivores themselves. To reduce these complications, we studied patch selection by an animal with negligible predation risk, the African elephant. 2. We introduce the concept of nutrient load as the product of patch size, number of patches and local patch nutrient concentration. Nutrient load provides a novel spatially explicit expression of the total available nutrients a herbivore can select from. 3. We hypothesized that elephant would select nutrient-rich patches, based on the nutrient load per 2500 m(2) down to the individual plant scale, and that this selection will depend on the nitrogen and phosphorous contents of plants. 4. We predicted that elephant would cause more adverse impact to trees of lower value to them in order to reach plant parts with higher nutrient concentrations such as bark and root. However, elephant should maintain nutrient-rich trees by inducing coppicing of trees through re-utilization of leaves. 5. Elephant patch selection was measured in a homogenous tree species stand by manipulating the spatial distribution of soil nutrients in a large field experiment using NPK fertilizer. 6. Elephant were able to select nutrient-rich patches and utilized Colophospermum mopane trees inside these patches more than outside, at scales ranging from 2500 down to 100 m(2) . 7. Although both nitrogen and phosphorus contents of leaves from C. mopane trees were higher in fertilized and selected patches, patch choice correlated most strongly with nitrogen content. As predicted, stripping of leaves occurred more in nutrient-rich patches, while adverse impact such as uprooting of trees occurred more in nutrient-poor areas. 8. Our results emphasize the necessity of including scale-dependent selectivity in foraging studies and how elephant foraging behaviour can be used as indicators of change in the availability of nutrients.

The spatial scaling of habitat selection by African elephants.

1. Understanding and accurately predicting the spatial patterns of habitat use by organisms is important for ecological research, biodiversity conservation and ecosystem management. However, this understanding is complicated by the effects of spatial scale, because the scale of analysis affects the quantification of species-environment relationships. 2. We therefore assessed the influence of environmental context (i.e. the characteristics of the landscape surrounding a site), varied over a large range of scales (i.e. ambit radii around focal sites), on the analysis and prediction of habitat selection by African elephants in Kruger National Park, South Africa. 3. We focused on the spatial scaling of the elephants' response to their main resources, forage and water, and found that the quantification of habitat selection strongly depended on the scales at which environmental context was considered. Moreover, the inclusion of environmental context at characteristic scales (i.e. those at which habitat selectivity was maximized) increased the predictive capacity of habitat suitability models. 4. The elephants responded to their environment in a scale-dependent and perhaps hierarchical manner, with forage characteristics driving habitat selection at coarse spatial scales, and surface water at fine spatial scales. 5. Furthermore, the elephants exhibited sexual habitat segregation, mainly in relation to vegetation characteristics. Male elephants preferred areas with high tree cover and low herbaceous biomass, whereas this pattern was reversed for female elephants. 6. We show that the spatial distribution of elephants can be better understood and predicted when scale-dependent species-environment relationships are explicitly considered. This demonstrates the importance of considering the influence of spatial scale on the analysis of spatial patterning in ecological phenomena.

rasterdiv-An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back.

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow.In this paper, we present a new R package-rasterdiv-to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms.

Priority list of biodiversity metrics to observe from space.

Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.